All of the mentioned scenarios may require handling missing values when calculating the median in R. For example, when analyzing survey data, it's common to have missing responses that need to be handled appropriately. Similarly, when calculating the median income, missing income values should be accounted for. Handling missing values ensures accurate median calculations and prevents biased results.

Starting with the installation of R and RStudio, the basics of R programming should be the first focus. Online resources, such as free tutorials, R documentation, or forums like Stack Overflow can be incredibly helpful. A mix of hands-on practice and theoretical learning usually works best.

Functions like subset(), filter(), and mutate() are specifically designed to operate on data frames in R. They allow for data manipulation, subsetting, and creating new variables within a data frame.

Some potential risks or downsides of using global variables in R include difficulty in tracking and managing dependencies between functions, increased potential for naming conflicts if multiple global variables have the same name, and reduced code modularity and reusability since functions become dependent on specific global variables. It is important to carefully manage and control the usage of global variables to minimize these risks.

Pie charts have the advantage of being easy to understand and visually appealing, making them suitable for displaying proportions. However, they have disadvantages such as being limited to a few categories, making it difficult to compare values accurately. Additionally, pie charts may not be suitable for showing hierarchical data or dealing with whole numbers, and they can be space-consuming. The choice of using a pie chart depends on the specific data and the purpose of visualization.

In R, the <- operator is commonly used to assign a value to a variable. For example, x <- 5 assigns the value 5 to the variable x. The = operator can also be used for assignment, but the <- operator is more commonly used.

In R, you can define a custom function called customMode() (or any preferred name) to calculate the mode of a numeric vector. This allows you to implement your own logic for identifying the mode based on the frequency of values.

For loops are generally slower than vectorized operations in R. R is optimized for vectorized operations, which can perform operations on entire vectors or matrices at once, leading to more efficient and faster execution.

R provides a wide range of statistical functions for mathematical computations. This includes functions to calculate the mean(), median(), mode(), minimum (min()), maximum (max()), sum(), standard deviation (sd()), variance (var()), correlation (cor()), and many others.

To create a scatter plot in R that shows the relationship between two numeric variables in a data set, you would use the plot() function. Specify the two numeric variables as the x and y arguments in the function call, and R will generate the scatter plot with the corresponding data points.

The concept of performing operations on entire vectors at once, without the need for looping over individual elements, is known as vectorization in R. It leverages optimized internal functions in R to apply operations to entire vectors efficiently, resulting in concise and computationally efficient code.

Yes, a global variable in R can be accessed from within a function. The scoping rules in R allow functions to access variables defined in the global environment. However, if a variable with the same name is defined within the function's local environment, it will take precedence over the global variable.